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Record W2186940966

Death for survival: what do we know about innate immunity and cell death in insects?

2011· article· en· W2186940966 on OpenAlexaff
Dawn Cooper, Kendra Mitchell‐Foster

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2011
Typearticle
Languageen
FieldImmunology and Microbiology
TopicInvertebrate Immune Response Mechanisms
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsBiologyImmunityInsectInnate immune systemImmune systemProgrammed cell deathImmunologyRepertoireEcologyZoologyEvolutionary biologyGenetics
DOInot available

Abstract

fetched live from OpenAlex

Insects are the most diverse and prolific animal group on Earth, and as such, important lessons can be taken from the elements that contribute to their evolutionary success. This review examines insect immunity and how insects combat infection with the pathogens they encounter: bacteria, viruses, fungi and parasites. Structural barriers, cellular and humoral responses and cell death all respond to specific immunological threats and contribute to the robust repertoire of immune strategies employed by insects. We discuss the strategies used by insects to combat pathogen infection and focus on what is currently known about cell death and its role in insect immunity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.209
GPT teacher head0.459
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2011
Admission routes1
Has abstractyes

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